Emotion prediction optimization method based on physical sign data

Abstract

The invention relates to an emotion prediction optimization method based on physical sign data, and relates to the field of health management, and the method comprises the steps: activating an adaptive local predictor, collecting local physical sign data, inputting the local physical sign data into the adaptive local predictor, and outputting a first prediction result; and if the first prediction result exceeds an emotion early warning threshold value and the number of continuous overflow times reaches a preset number limit, activating an adaptive global predictor, inputting the global sign data into the adaptive global predictor, and outputting a second prediction result as an emotion monitoring result of the target user. Through the method, the technical problem that a traditional emotion prediction method is difficult to consider the real-time performance and prediction accuracy of emotion prediction at the same time and cannot meet the current emotion prediction demand can be solved, and rapid response and deep analysis in different emotion states can be realized, so that the real-time performance and efficiency are ensured, and the emotion prediction efficiency is improved. The emotion prediction precision and reliability are effectively improved, and the computing power resource consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the field of health management, and in particular to an emotion prediction optimization method based on physical sign data. Background Art

[0002] Emotion prediction technology is based on physiological and behavioral characteristics (such as heart rate, skin galvanic response, breathing rate, etc.) to identify and analyze the emotional state of an individual. With the widespread application of smart wearable devices, health monitoring systems and mental health applications, the demand for emotion prediction is growing. However, traditional emotion prediction methods usually either use simple prediction models to ensure real-time performance, but sacrifice the accuracy of prediction; or rely on complex large models to improve accuracy, but result in slow system response and high computational consumption, which cannot meet the needs of real-time monitoring.

[0003] In summary, traditional emotion prediction methods are difficult to take into account both the real-time and prediction accuracy of emotion prediction, and cannot meet the current emotion prediction needs. Summary of the invention

[0004] The present invention aims to solve the technical problem that traditional emotion prediction methods are difficult to take into account both the real-time performance and prediction accuracy of emotion prediction and cannot meet the current emotion prediction needs, and provides an emotion prediction optimization method based on vital sign data to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides an emotion prediction optimization method based on vital sign data, comprising: building an emotion prediction plug-in matching table based on a predetermined basic parameter interval, a predetermined motion parameter interval, and a predetermined environmental parameter interval, wherein the emotion prediction plug-in comprises a local emotion predictor and a global emotion predictor, the local emotion predictor being constructed based on local vital sign indicators, and the global emotion predictor being constructed based on global vital sign indicators; inputting the basic data, expected motion data, and environmental data of a target user into the emotion prediction plug-in matching table to obtain an adapted local predictor and an adapted global predictor; activating the adapted local predictor, collecting local vital sign data according to the local vital sign indicators and inputting the adapted local predictor, and outputting a first prediction result; if the first prediction result exceeds an emotion warning threshold, and the number of consecutive overflows reaches a predetermined number limit, activating the adapted global predictor, collecting global vital sign data according to the global vital sign indicators and inputting the adapted global predictor, and outputting a second prediction result as an emotion monitoring result of the target user.

[0007] In a second aspect, the present invention further provides an electronic device, comprising:

[0008] At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.

[0009] In a third aspect, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed, the steps of the method described in any one of the first aspects are implemented.

[0010] The beneficial effects of the present invention are as follows: by building an emotion prediction plug-in matching table based on a predetermined basic parameter interval, a predetermined motion parameter interval and a predetermined environmental parameter interval, wherein the emotion prediction plug-in includes a local emotion predictor and a global emotion predictor, the local emotion predictor is built based on a local vital sign index, and the global emotion predictor is built based on a global vital sign index; inputting the target user's basic data, expected motion data and environmental data into the emotion prediction plug-in matching table to obtain an adapted local predictor and an adapted global predictor; activating the adapted local predictor, collecting local vital sign data according to the local vital sign index and inputting it into the adapted local predictor, and outputting a first prediction result; if the When the first prediction result exceeds the emotion warning threshold and the number of consecutive overflows reaches a predetermined number limit, the adapted global predictor is activated, global vital sign data is collected according to the global vital sign index and input into the adapted global predictor, and a second prediction result is output as the emotion monitoring result of the target user; that is, by adopting a local emotion prediction model for short-term and efficient monitoring, it is possible to quickly respond to emotional anomalies, and switch to a global emotion prediction model for precise analysis when the abnormal emotion persists, which can achieve rapid response and in-depth analysis under different emotional states, thereby effectively improving the accuracy and reliability of emotion prediction while ensuring real-time and efficiency, and saving computing power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a flow chart of an emotion prediction optimization method based on physical sign data provided by the present invention;

[0012] Figure 2 A schematic diagram of the structure of an electronic device provided by the present invention;

[0013] Figure 3 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0014] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0015] Electronic device 500 , memory 510 , processor 520 , first computer program 511 , computer-readable storage medium 600 , second computer program 611 . DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0019] Embodiment 1, as Figure 1 As shown, the embodiment of the present invention provides an emotion prediction optimization method based on vital sign data, which specifically includes the following steps:

[0020] An emotion prediction plug-in matching table is constructed based on a predetermined basic parameter interval, a predetermined motion parameter interval, and a predetermined environmental parameter interval, wherein the emotion prediction plug-in includes a local emotion predictor and a global emotion predictor, the local emotion predictor is constructed based on local vital sign indicators, and the global emotion predictor is constructed based on global vital sign indicators.

[0021] Further, to construct a global emotion predictor, the present application also includes:

[0022] A vital sign indicator space is configured, wherein the vital sign indicator space includes a number of individual sign indicators, and the vital sign indicators include at least heart rate, galvanic skin response, respiratory rate, blood oxygen saturation, body temperature, blood pressure, brain waves, etc.; collection convenience and emotion correlation analysis are performed on the several individual sign indicators respectively, and a number of collection convenience coefficients and a number of emotion correlation coefficients are obtained, and a number of indicator influences are obtained by weighted calculation; the several indicator influences are screened according to a predetermined influence threshold, and the physical sign indicators corresponding to the indicator influences greater than the predetermined influence threshold are selected to obtain a global physical sign indicator.

[0023] Specifically, in emotion prediction and health monitoring, configuring a suitable physical sign indicator space is the basis for achieving efficient and accurate prediction. The physical sign indicator space includes multiple physiological and biological signals. These indicators work together to provide multi-dimensional information about the individual's emotional state. First, configure the physical sign indicator space, where the physical sign indicator space includes several individual sign indicators. The physical sign indicators include at least heart rate, skin electrical response, respiratory rate, blood oxygen saturation, body temperature, blood pressure, brain waves, etc. For example, heart rate reflects the number of heart beats per minute, which is usually closely related to emotional changes. For example, in emotional states such as anxiety, fear or anger, the heart rate will accelerate; when relaxed, the heart rate will be slower.

[0024] Next, the convenience of collection and emotional correlation of the several physical sign indicators are analyzed respectively, wherein the convenience of collection refers to the difficulty of measuring each physical sign indicator, the equipment requirements and the feasibility of real-time collection. Indicators with higher convenience usually mean that they can be monitored on low-cost and efficient equipment, such as through smart bracelets or wearable devices, and the convenience coefficient of collection is used to measure this attribute; for example, heart rate monitoring is usually carried out through photoelectric volume pulse wave (PPG) technology, which is convenient, and many smart devices support real-time monitoring, and the convenience coefficient is high; EEG monitoring requires special electrodes and equipment, which is usually more complicated. The convenience coefficient is low. Emotional correlation refers to the strength of the correlation between physical sign indicators and emotional changes, which is quantified by the emotional correlation coefficient, usually based on physiological research or data analysis. For example, heart rate is closely related to emotional changes, especially in emotions such as anxiety, anger and stress, the heart rate usually accelerates, and the emotional correlation coefficient is high; respiratory rate has obvious fluctuations in tension, anxiety or relaxation, especially when emotional fluctuations occur, and the emotional correlation coefficient is medium; blood oxygen saturation is indirectly related to emotional fluctuations, mainly by affecting changes in breathing patterns, and the emotional correlation coefficient is low.

[0025] Then, weights are configured for the collection convenience coefficient and the emotion correlation coefficient respectively, which can be adjusted according to the needs to ensure the balance between convenience and emotion correlation in the calculation; then, according to the configured weights, several collection convenience coefficients and several emotion correlation coefficients are weighted and calculated to obtain several indicator influences, wherein the indicators with high influence can be used as the focus of the emotion prediction model. Further, the influences of the several indicators are screened according to the predetermined influence threshold, wherein, in order to screen out the most important global vital signs indicators, a predetermined influence threshold needs to be set, which is used to filter out those indicators with low influence, ensuring that only the vital signs indicators that have a significant contribution to emotion prediction are selected, and a suitable influence threshold can be selected according to the actual application requirements, equipment resource limitations and accuracy requirements. For example, the threshold can be set to 0.5, which means that the vital signs indicators with an influence less than 0.5 will be excluded. Then, the vital signs indicators corresponding to the indicator influence greater than the predetermined influence threshold are selected to obtain the global vital signs indicators, which refer to those indicators that have a greater contribution to emotion prediction, are convenient to collect and have a high correlation with the emotional state.

[0026] Based on the principle of integrated learning, the global emotion predictor is constructed according to the global vital sign indicators.

[0027] Furthermore, the present application also includes the following steps:

[0028] Randomly select any parameter in the predetermined basic parameter interval, the predetermined motion parameter interval and the predetermined environmental parameter interval for combination to obtain a first scene parameter; perform retrieval with the first scene parameter and the global sign index as constraints to obtain a sample global sign index set and a sample emotion coefficient set; use the sample global sign index set and the sample emotion coefficient set as training data to supervise N emotion prediction operators to obtain N global emotion prediction branches that meet convergence, wherein the emotion prediction operator includes at least a BP neural network, a support vector machine and a random decision forest, and N is an integer greater than or equal to 3; obtain N prediction accuracies of the N global emotion prediction branches, and set N credible weight coefficients according to the N prediction accuracies; based on the principle of ensemble learning, construct a first global emotion predictor according to the N global emotion prediction branches and the N credible weight coefficients, and establish a first mapping association between the first scene parameter and the first global emotion predictor.

[0029] Specifically, in the emotion prediction optimization method based on vital sign data, the predetermined basic parameter interval, motion parameter interval and environmental parameter interval are used to make personalized emotion predictions based on the user's individual characteristics, activity status and environmental conditions. The acquisition and setting of these parameters help improve the accuracy and adaptability of emotion prediction.

[0030] First, a predetermined basic parameter interval, a predetermined motion parameter interval, and a predetermined environmental parameter interval are obtained, wherein the basic parameters refer to basic physical parameters, such as age, gender, height, weight, etc. For example, the age interval is set to different intervals such as 0 to 18 years old, 19 to 35 years old, 36 to 60 years old, and over 60 years old; the motion parameters include the type of exercise, the intensity of exercise, etc.; the environmental parameters include the ambient temperature, etc. Then, any parameter is randomly selected from the predetermined basic parameter interval, the predetermined motion parameter interval, and the predetermined environmental parameter interval to combine and obtain the first scene parameter, for example, the first scene parameter includes 19 to 35 years old, male, 175 cm, running, room temperature 25 to 30 degrees Celsius, etc.

[0031] Next, information retrieval is performed with the first scene parameters and global vital sign indicators as constraints to screen out sample data that meets the needs of emotion prediction. Information retrieval can be performed through big data technology to obtain a sample global vital sign indicator set and a sample emotion coefficient set, where the emotion coefficient can be calculated based on historical data, user feedback or emotion labels, indicating the intensity of the emotional state. For example, 0 represents no emotion, 10 represents an extreme emotional state, the angry emotion coefficient can be set to a higher value (such as 8 or 9), and the calm emotion coefficient to a lower value (such as 2 or 3).

[0032] Then, obtain N emotion prediction operators, where the emotion prediction operators include at least BP neural network, support vector machine and random decision forest, N is an integer greater than or equal to 3, BP neural network is a commonly used feedforward neural network, trained by back propagation algorithm, and can handle nonlinear relationships; SVM is a powerful classification and regression model, especially suitable for classification tasks of high-dimensional data. SVM optimizes classification accuracy by finding the maximum margin hyperplane; random decision forest performs classification and regression through the voting mechanism of multiple decision trees, has strong robustness, and can handle high-noise data; technical personnel in this field can also select an adaptive prediction operator according to actual conditions; further use the sample global sign indicator set and the sample emotion coefficient set as training data, and divide the training data into N parts to obtain N training data sets; then, use the sample global sign indicator as input and the sample emotion coefficient as output, and use N data sets to supervise the training of N emotion prediction operators respectively. For example, construct a first global emotion prediction branch based on the BP neural network, and use the first training data set to supervise the training of the first global emotion prediction branch. First, initialize the structure and parameters of the BP neural network, including input The number of neurons in the forward layer, hidden layer and output layer is randomly initialized with weights and biases; then, the input (global vital sign indicator set) in the training data set is passed into the neural network, and calculations are performed through each layer to finally obtain the prediction result; then, according to the gap between the predicted value and the actual emotion coefficient, the loss function (such as mean square error MSE) is calculated, and the gradient of the loss function to the network parameters (weights and biases) is calculated through the back propagation algorithm, and these parameters are updated, and the gradient descent algorithm (or other optimization algorithms) is used to minimize the loss function; the above process (forward propagation, calculation of loss, back propagation, parameter update) is repeated until the loss function converges to the minimum value, or the preset maximum number of training rounds is reached; the first global emotion prediction branch that has completed training is output; and then N global emotion prediction branches that meet convergence are obtained in turn.

[0033] Further obtain N prediction accuracies of the N global emotion prediction branches, where prediction accuracy refers to the accuracy of the model output; then, according to the prediction accuracy of each emotion prediction branch, a credible weight coefficient can be assigned to it, and branches with higher prediction accuracy will be given a larger weight, and vice versa, a smaller weight will be given, and N credible weight coefficients are obtained. Finally, based on the principle of ensemble learning, a first global emotion predictor is constructed according to the N global emotion prediction branches and N credible weight coefficients, that is, according to the principle of ensemble learning, the output results of the N emotion prediction branches are synthesized into a final prediction result using a weighted average method. And establish a first mapping association between the first scene parameter and the first global emotion predictor.

[0034] By building a global emotion predictor based on the principle of ensemble learning, that is, combining the output results of multiple prediction models for comprehensive prediction, the prediction bias of a single model can be reduced and the stability and accuracy of emotion prediction can be improved.

[0035] Further, to construct a local emotion predictor, the present application also includes:

[0036] In the two-dimensional space, a correlation fluctuation curve between the global physical sign index and the emotional characteristics is constructed to obtain a plurality of global correlation fluctuation curves.

[0037] Specifically, in the emotion prediction optimization method, the relationship between global vital signs (such as heart rate, skin galvanic response, breathing rate, etc.) and emotional characteristics (such as emotional intensity, emotional labels, etc.) can be represented by associated fluctuation curves. By constructing these curves, the dynamic association and change trend between vital sign data and emotional state can be revealed. Then, in a two-dimensional space, the associated fluctuation curves of the global vital signs and emotional characteristics are constructed based on the sample data to obtain multiple global associated fluctuation curves. For example, in a two-dimensional space, the X-axis represents the global vital signs (such as heart rate, skin galvanic response, etc.), each vital sign has a corresponding value, and the Y-axis represents the emotional characteristics (such as emotional intensity or emotional label). The emotional coefficient can be a continuous value (such as emotional intensity), or use emotional labels (such as happiness, anxiety, etc.). Based on the sample data, in chronological order, the associated fluctuation curve is constructed. The key to this process is to correspond the vital signs and emotional characteristics at each time point, and connect them to form a fluctuation curve. By observing the fluctuation of the curve, the dynamic relationship between the vital signs and emotional changes can be analyzed.

[0038] Performing similarity dimensionality reduction on the multiple global correlation fluctuation curves to obtain multiple difference correlation fluctuation curves, setting the physical sign indicators corresponding to the multiple difference correlation fluctuation curves as difference physical sign indicators, and obtaining multiple difference physical sign indicators;

[0039] Furthermore, the present application also includes the following steps:

[0040] A first associated fluctuation curve is randomly selected from the multiple global associated fluctuation curves, and a similarity comparison is performed on the first associated fluctuation curve and other associated fluctuation curves in the multiple global associated fluctuation curves to obtain multiple first similarities; the number of first similarities less than a predetermined similarity index is counted and set as a first difference; if the first difference is greater than the predetermined difference index, the first associated fluctuation curve is set as a difference associated fluctuation curve and added to the multiple difference associated fluctuation curves.

[0041] Specifically, any one of the multiple global associated fluctuation curves is randomly selected as the first associated fluctuation curve; then, the first associated fluctuation curve and other associated fluctuation curves in the multiple global associated fluctuation curves are respectively compared for similarity, for example, similarity comparison can be performed by Euclidean distance, which is a standard way to measure the straight-line distance between two points, and is used to measure the difference between two curves at each corresponding time point. The closer the value is to 1, the closer the two curves are; and multiple first similarities are obtained. Further, the number of first similarities less than the predetermined similarity index is counted, and the predetermined similarity threshold can be set according to the actual application scenario. For example, if the similarity is less than 0.7, it is considered to be a large difference, and the quantitative statistical result is set as the first difference, which characterizes the overall deviation of the first associated fluctuation curve from other associated fluctuation curves in the multiple global associated fluctuation curves. If the first difference is greater than the predetermined difference index (which can be set according to the number of indicators, such as 5), the first associated fluctuation curve is set as a difference associated fluctuation curve and added to the multiple difference associated fluctuation curves.

[0042] Finally, the physical sign indicators corresponding to the plurality of difference-associated fluctuation curves are set as difference physical sign indicators to obtain a plurality of difference physical sign indicators. By similarity comparison based on Euclidean distance, the difference-associated fluctuation curves are screened out, and the corresponding difference physical sign indicators are extracted, so that the physical sign data with significant differences in the process of emotion change can be effectively identified.

[0043] The plurality of difference physical sign indicators are sorted in descending order according to the degree of influence of the indicators, and a preset number of indicators in the difference physical sign indicator sequence are selected as local physical sign indicators.

[0044] Specifically, the plurality of difference physical sign indicators are further sorted from large to small according to the degree of influence of the indicators to obtain a difference physical sign indicator sequence; then a preset number of indicators (such as the first 3) in the difference physical sign indicator sequence are selected as local physical sign indicators. By sorting the degree of influence of the difference physical sign indicators and selecting the first N indicators as local physical sign indicators, it is possible to ensure that the emotion prediction system pays attention to the most critical physical sign data while providing high real-time prediction. This method combines short-term monitoring and high efficiency, and effectively improves the overall performance of the system while ensuring the accuracy of emotion prediction.

[0045] Based on the BP neural network, a local emotion predictor is constructed according to the local physical sign indicators.

[0046] Furthermore, the present application also includes the following steps:

[0047] Perform a search with the first scene parameter and local sign indicators as constraints to obtain a sample local sign indicator set and a sample emotion coefficient set; configure Q local prediction branches based on the BP neural network, where Q is an integer greater than 1 and less than 5; use the sample local sign indicator set and the sample emotion coefficient set as a sample data set, and divide the sample data set into Q equal parts, select Q parts with replacement to construct a first training set, iterate and select Q times to obtain Q training sets; use the Q training sets to perform supervised training on the Q local prediction branches until convergence, construct a first local emotion predictor based on the trained Q local prediction branches, and establish a second mapping association between the first scene parameter and the first local emotion predictor.

[0048] Specifically, in the emotion prediction optimization method, the local emotion predictor is used to quickly predict emotions based on specific physical sign indicators; first, the first scene parameters and local physical sign indicators are used as constraints for retrieval to obtain a sample local physical sign indicator set and a sample emotion coefficient set; then, Q local prediction branches are configured based on the BP neural network, where Q is an integer greater than 1 and less than 5; then the sample local physical sign indicator set and the sample emotion coefficient set are used as a sample data set, and the sample data set is divided into Q equal parts to obtain Q data sets, and the Q data sets are selected Q times with replacement to construct a first training set, and the same method is used to iteratively select Q times to obtain Q training sets.

[0049] Finally, the Q local prediction branches are supervised and trained by taking the sample local sign index as input and the sample emotion coefficient as supervision using the Q training sets. First, the BP neural network is used for training. Each local prediction branch is independently learned. Each local prediction branch learns how to predict the target emotion coefficient according to the input local sign index. Then, a standard loss function (such as mean square error MSE) is used to evaluate the gap between the prediction result of each local prediction branch and the true label, and the gradient descent algorithm is used to adjust the network parameters until the network converges, that is, the value of the loss function approaches the minimum. When the loss function of each local prediction branch reaches a predetermined convergence standard (such as the error is less than a certain threshold, or the number of training iterations reaches a predetermined value), the training process stops. After the training is completed, Q local emotion prediction branches are obtained. Then, the first local emotion predictor is constructed based on the trained Q local prediction branches, and a second mapping association between the first scene parameter and the first local emotion predictor is established.

[0050] Furthermore, the present application also includes:

[0051] Based on the first mapping association and the second mapping association, the emotion prediction plug-in matching table is constructed according to the first scene parameter, the first global emotion predictor and the first local emotion predictor.

[0052] Specifically, based on the classification decision principle, the first mapping association and the second mapping association are used to construct a first matching relationship according to the first scene parameter, the first global emotion predictor and the first local emotion predictor. The matching relationship can dynamically call the adapted emotion prediction plug-in (including the local emotion predictor and the global emotion predictor) according to the specific first scene parameter to ensure that the emotion prediction system makes the most reasonable decision based on the user's real-time data and emotional state. Then, using the same method, multiple scene parameters and multiple global emotion predictors and multiple local emotion predictors corresponding to the multiple scene parameters are obtained in turn, multiple matching relationships are constructed, and the emotion prediction plug-in matching table is constructed based on the multiple matching relationships.

[0053] By constructing an emotion prediction plug-in matching table based on the classification decision principle, the appropriate emotion predictor can be intelligently selected according to the different characteristics of the scene parameters. This method not only improves the efficiency and real-time performance of emotion prediction, but also optimizes the accuracy and reliability of emotion prediction by dynamically adapting different predictors.

[0054] The target user's basic data, expected motion data, and environmental data are input into the emotion prediction plug-in matching table to obtain an adapted local predictor and an adapted global predictor.

[0055] Specifically, the basic data (height, weight, etc.), expected motion data (exercise to be performed) and environmental data (ambient temperature, etc.) of the target user are collected; and the basic data, expected motion data and environmental data are input into the emotion prediction plug-in matching table for matching to obtain an adapted local predictor and an adapted global predictor.

[0056] The adapted local predictor is activated, local vital sign data is collected according to the local vital sign index and input into the adapted local predictor, and a first prediction result is output.

[0057] Specifically, the adapted local predictor is then activated; local vital sign data of the target user is collected according to the local vital sign index, and the local vital sign data is input into any prediction branch of the adapted local predictor for prediction, wherein, in order to save computing resources, prediction is initially performed through only a single branch, and a first prediction result is output. By activating only a single prediction branch, the computing resource consumption of the system is reduced, especially in the emotion prediction process, a preliminary prediction result can be quickly obtained without sacrificing real-time performance.

[0058] If the first prediction result exceeds the emotion warning threshold and the number of consecutive overflows reaches a predetermined limit, the adapted global predictor is activated, global vital sign data is collected according to the global vital sign index and input into the adapted global predictor, and a second prediction result is output as the emotion monitoring result of the target user.

[0059] Furthermore, the present application also includes:

[0060] Obtain a first prediction result, wherein the first prediction result is a first predicted emotion coefficient; if the first predicted emotion coefficient is greater than the emotion warning threshold, calculate the emotion coefficient deviation, and set the ratio of the emotion coefficient deviation to the maximum emotion coefficient deviation as the emotion fluctuation scale; round the emotion fluctuation scale and multiply it by Q to obtain the number of calls of the local prediction branch, wherein the initial number of call branches of the adapted local predictor is 1; randomly select the local prediction branches of the called number from the Q local prediction branches of the adapted local predictor, perform continuous monitoring, and record the number of consecutive overflows, wherein overflow refers to the predicted emotion coefficient being greater than the emotion warning threshold, and during the continuous monitoring process, the number of local prediction branches will be iteratively updated according to the emotion coefficient deviation in each monitoring result; if the number of consecutive overflows reaches a predetermined number limit, activate the adapted global predictor.

[0061] Specifically, the first prediction result is obtained, wherein the first prediction result is the first predicted emotion coefficient, which indicates the intensity of the current emotional state of the target user; then the first predicted emotion coefficient is judged according to the emotion warning threshold, wherein the emotion warning threshold is used to judge whether the emotion fluctuation reaches the level that requires intervention, which can be set according to the actual scenario. If the first predicted emotion coefficient exceeds the threshold, it means that the user may be experiencing significant emotion fluctuations (such as anxiety, excessive stress, etc.); if the first predicted emotion coefficient is greater than the emotion warning threshold, the first predicted emotion coefficient is subtracted from the emotion warning threshold, and the difference between the two is used as the emotion coefficient deviation, which indicates the difference between the user's current emotion and his normal state, and is used to evaluate the degree of current emotion fluctuations. The ratio of the emotion coefficient deviation to the maximum emotion coefficient deviation (which can be set based on historical data) is further calculated, and the ratio is set as the emotion fluctuation scale.

[0062] Then, the scale of the emotion fluctuation is rounded and multiplied by Q to obtain the number of calls of the local prediction branch, that is, the greater the emotion fluctuation, the number of prediction branches used will increase accordingly, wherein the initial number of call branches adapted to the local predictor is 1, that is, only one branch is involved in the prediction in the initial state of the system to save computing resources. Further, among the Q local prediction branches, the system will randomly select branches with the same number of calls for continuous monitoring, which can ensure the diverse use of multiple branches and avoid calling all branches every time, saving computing resources. During each monitoring, the system will record the emotion coefficient deviation and compare it. If the prediction result (emotion coefficient) exceeds the emotion warning threshold, it is considered that an overflow has occurred. During the monitoring process, the number of local prediction branches will be iteratively updated according to the emotion coefficient deviation in each monitoring result.

[0063] During the continuous monitoring process, if the emotion coefficient exceeds the warning threshold for multiple times, the number of continuous overflows, that is, the number of consecutive times of abnormal emotions, is counted; when the number of continuous overflows reaches the predetermined number limit (such as 3 times), it means that the user's emotional abnormality is relatively continuous and cannot be solved by the local predictor. At this time, the system will start a more accurate emotion prediction model, and then activate the adapted global predictor, that is, when the emotion fluctuation of the local predictor continues to exceed the predetermined threshold, and after monitoring the number of continuous overflows, the system will automatically switch to the global predictor for more accurate emotion analysis. Then, according to the global sign index, the global sign data of the target user is collected and input into the adapted global predictor, and the second prediction result (second predicted emotion coefficient) is output as the emotion monitoring result of the target user.

[0064] The embodiment of the present invention provides an emotion prediction optimization method based on vital sign data, which has at least the following technical effects:

[0065] By adopting the local emotion prediction model for short-term and efficient monitoring, we can quickly respond to emotional anomalies, and switch to the global emotion prediction model for accurate analysis when the abnormal emotions persist. This can achieve rapid response and in-depth analysis under different emotional states, thereby effectively improving the accuracy and reliability of emotion prediction while ensuring real-time and efficiency, and saving computing power resources.

[0066] For example 2, please refer to Figure 2 , Figure 2 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: an emotion prediction plug-in matching table is constructed based on a predetermined basic parameter interval, a predetermined motion parameter interval, and a predetermined environmental parameter interval, wherein the emotion prediction plug-in includes a local emotion predictor and a global emotion predictor, the local emotion predictor is constructed based on a local vital sign index, and the global emotion predictor is constructed based on a global vital sign index; basic data, expected motion data, and environmental data of a target user are input into the emotion prediction plug-in matching table to obtain an adapted local predictor and an adapted global predictor; the adapted local predictor is activated, local vital sign data is collected according to the local vital sign index and input into the adapted local predictor, and a first prediction result is output; if the first prediction result exceeds the emotion warning threshold, and the number of consecutive overflows reaches a predetermined number limit, the adapted global predictor is activated, global vital sign data is collected according to the global vital sign index and input into the adapted global predictor, and a second prediction result is output as the emotion monitoring result of the target user.

[0067] For example 3, please refer to Figure 3 , Figure 3 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 3 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, the following steps are implemented: constructing an emotion prediction plug-in matching table based on a predetermined basic parameter interval, a predetermined motion parameter interval, and a predetermined environmental parameter interval, wherein the emotion prediction plug-in includes a local emotion predictor and a global emotion predictor, the local emotion predictor is constructed based on a local vital sign indicator, and the global emotion predictor is constructed based on a global vital sign indicator; inputting the basic data, expected motion data, and environmental data of the target user into the emotion prediction plug-in matching table to obtain an adapted local predictor and an adapted global predictor; activating the adapted local predictor, collecting local vital sign data according to the local vital sign indicator and inputting the adapted local predictor, and outputting a first prediction result; if the first prediction result exceeds the emotion warning threshold, and the number of consecutive overflows reaches a predetermined number limit, activating the adapted global predictor, collecting global vital sign data according to the global vital sign indicator and inputting the adapted global predictor, and outputting a second prediction result as the emotion monitoring result of the target user.

[0068] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0073] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for optimizing emotion prediction based on vital sign data, characterized in that: Methods include: Building an emotion prediction plug-in matching table based on a predetermined basic parameter interval, a predetermined motion parameter interval, and a predetermined environmental parameter interval, wherein the emotion prediction plug-in includes a local emotion predictor and a global emotion predictor, the local emotion predictor is built based on a local sign indicator, and the global emotion predictor is built based on a global sign indicator; Inputting the target user's basic data, expected motion data, and environmental data into the emotion prediction plug-in matching table to obtain an adapted local predictor and an adapted global predictor; activating the adapted local predictor, collecting local vital sign data according to the local vital sign index, inputting the data into the adapted local predictor, and outputting a first prediction result; If the first prediction result exceeds the emotion warning threshold and the number of consecutive overflows reaches a predetermined limit, the adapted global predictor is activated, global vital sign data is collected according to the global vital sign index and input into the adapted global predictor, and a second prediction result is output as the emotion monitoring result of the target user.

2. The emotion prediction optimization method based on vital sign data according to claim 1, characterized in that: Build a global sentiment predictor, including: Configuring a physical sign index space, wherein the physical sign index space includes a number of physical sign indexes, and the physical sign indexes at least include heart rate, skin galvanic response, respiratory rate, blood oxygen saturation, body temperature, blood pressure, brain waves, etc.; Performing collection convenience and emotion correlation analysis on the several individual characteristic indicators respectively, obtaining several collection convenience coefficients and several emotion correlation coefficients, and performing weighted calculation to obtain several indicator influences; Screening the influences of the plurality of indicators according to a predetermined influence threshold, selecting the physical sign indicators corresponding to the influences of the indicators that are greater than the predetermined influence threshold, and obtaining the global physical sign indicators; Based on the principle of integrated learning, the global emotion predictor is constructed according to the global vital sign indicators.

3. The emotion prediction optimization method based on vital sign data according to claim 2, characterized in that: Based on the principle of integrated learning, the global emotion predictor is constructed according to the global vital sign index, including: Randomly select any parameter in the predetermined basic parameter interval, the predetermined motion parameter interval, and the predetermined environment parameter interval to combine and obtain a first scene parameter; Performing a search with the first scene parameter and the global vital sign index as constraints to obtain a sample global vital sign index set and a sample emotion coefficient set; Using the sample global sign indicator set and the sample emotion coefficient set as training data, supervised training is performed on N emotion prediction operators to obtain N global emotion prediction branches that meet convergence requirements, wherein the emotion prediction operators include at least a BP neural network, a support vector machine, and a random decision forest, and N is an integer greater than or equal to 3; Obtaining N prediction accuracies of the N global emotion prediction branches, and setting N credible weight coefficients according to the N prediction accuracies; Based on the principle of integrated learning, a first global emotion predictor is constructed according to the N global emotion prediction branches and the N credible weight coefficients, and a first mapping association between the first scene parameter and the first global emotion predictor is established.

4. The emotion prediction optimization method based on vital sign data according to claim 3, characterized in that: Build a local sentiment predictor, including: In the two-dimensional space, constructing a correlation fluctuation curve between the global physical sign index and the emotional characteristics to obtain a plurality of global correlation fluctuation curves; Performing similarity dimensionality reduction on the multiple global correlation fluctuation curves to obtain multiple difference correlation fluctuation curves, setting the physical sign indicators corresponding to the multiple difference correlation fluctuation curves as difference physical sign indicators, and obtaining multiple difference physical sign indicators; Sorting the plurality of difference physical sign indicators in descending order according to the degree of influence of the indicators, and selecting a preset number of indicators in the difference physical sign indicator sequence as local physical sign indicators; Based on the BP neural network, a local emotion predictor is constructed according to the local physical sign indicators.

5. The emotion prediction optimization method based on vital sign data according to claim 4, characterized in that: The multiple global correlation fluctuation curves are subjected to similarity dimensionality reduction to obtain multiple difference correlation fluctuation curves, including: randomly selecting a first associated fluctuation curve from the multiple global associated fluctuation curves, and performing similarity comparison on the first associated fluctuation curve and other associated fluctuation curves from the multiple global associated fluctuation curves to obtain multiple first similarities; Count the number of first similarities less than a predetermined similarity index and set it as a first difference; If the first difference is greater than a predetermined difference index, the first associated fluctuation curve is set as a difference associated fluctuation curve, and is added to the plurality of difference associated fluctuation curves.

6. The emotion prediction optimization method based on vital sign data according to claim 4, characterized in that: Based on the BP neural network, a local emotion predictor is constructed according to the local physical sign indicators, including: Performing a search with the first scene parameter and the local physical sign index as constraints to obtain a sample local physical sign index set and a sample emotion coefficient set; Based on the BP neural network, Q local prediction branches are configured, where Q is an integer greater than 1 and less than 5; The sample local sign indicator set and the sample emotion coefficient set are used as a sample data set, and the sample data set is equally divided into Q parts, and the first training set is constructed by selecting Q parts with replacement, and the first training set is constructed by iterative selection Q times to obtain Q training sets; The Q local prediction branches are supervisedly trained using the Q training sets until convergence, a first local emotion predictor is constructed based on the trained Q local prediction branches, and a second mapping association between the first scene parameter and the first local emotion predictor is established.

7. The emotion prediction optimization method based on vital sign data according to claim 6, characterized in that: Based on the first mapping association and the second mapping association, the emotion prediction plug-in matching table is constructed according to the first scene parameter, the first global emotion predictor and the first local emotion predictor.

8. The emotion prediction optimization method based on vital sign data according to claim 6, characterized in that: If the first prediction result exceeds the emotion warning threshold and the number of consecutive overflows reaches a predetermined number limit, activating the adapted global predictor includes: Obtaining a first prediction result, wherein the first prediction result is a first prediction emotion coefficient; If the first predicted emotion coefficient is greater than the emotion warning threshold, the emotion coefficient deviation is calculated, and the ratio of the emotion coefficient deviation to the maximum emotion coefficient deviation is set as the emotion fluctuation scale; The emotional fluctuation scale is rounded and multiplied by Q to obtain the number of calls of the local prediction branch, wherein the number of initial call branches of the adapted local predictor is 1; Randomly select the local prediction branches of the called number from the Q local prediction branches of the adapted local predictor, perform continuous monitoring, and record the number of continuous overflows, wherein overflow means that the predicted emotion coefficient is greater than the emotion warning threshold, and the number of local prediction branches in the continuous monitoring process will be iteratively updated according to the emotion coefficient deviation in each monitoring result; If the number of consecutive overflows reaches a predetermined limit, the adaptive global predictor is activated.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of an emotion prediction optimization method based on vital sign data as described in any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, which, when executed by a processor, implements the steps of an emotion prediction optimization method based on vital sign data as described in any one of claims 1 to 8.

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